ArgoTweak: Towards Self-Updating HD Maps through Structured Priors

Lena Wild, Rafael Valencia, Patric Jensfelt

About

Reliable integration of prior information is crucial for selfverifying and self-updating HD maps. However, no public dataset includes the required triplet of prior maps, current maps, and sensor data. As a result, existing methods must rely on synthetic priors, which create inconsistencies and lead to a significant sim2real gap. To address this, we introduce ArgoTweak, the first dataset to complete the triplet with realistic map priors. At its core, ArgoTweak employs a bijective mapping framework, breaking down large-scale modifications into fine-grained atomic changes at the map element level, thus ensuring interpretability. This paradigm shift enables accurate change detection and integration while preserving unchanged elements with high fidelity.

License

This dataset is derived from the Argoverse 2 Map Change Dataset, originally released by Argo AI under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Our annotations are licensed under the same terms.

[v1.0.0] – 2025-08-01

🚀 Official release of the ArgoTweak dataset The numbers in the paper have been achieved with a legacy version (v0) of ArgoTweak. The official version v1 has been improved as follows:

• Refined lane segment connectivities (predecessors/successors) in multiple scenes
• Annotated changes in the test split that were not identified in the original dataset https://github.com/johnwlambert/tbv/blob/main/labeled_data/tbv_val_split_annotations.json
• Improved consistencies of geometry annotations in several maps
• Removed incorrect intersection status of isolated lane segments

If, for any reason, you require access to the legacy version (v0) of ArgoTweak, please contact the authors directly.

Quick Start

Please refer to our ✨dataset page✨ for more detail.

Citation

@inproceedings{wild2025argotweak,
  title={ArgoTweak: Towards Self-Updating HD Maps through Structured Priors},
  author={Lena Wild and Rafael Valencia and Patric Jensfelt},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2025}
}